%0 Journal Article
%J Science of The Total Environment
%D 2014
%T Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver
%A Money, Eric S.
%A Barton, Lauren E.
%A Dawson, Joseph
%A Reckhow, Kenneth H.
%A Wiesner, Mark R.
%P 685 - 691
%R 10.1016/j.scitotenv.2013.12.100
%V 473-474
%X The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.
%8 03/2014